Overview

Dataset statistics

Number of variables21
Number of observations3333
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory524.2 KiB
Average record size in memory161.0 B

Variable types

Categorical3
Numeric15
Boolean3

Alerts

state has a high cardinality: 51 distinct valuesHigh cardinality
phone number has a high cardinality: 3333 distinct valuesHigh cardinality
number vmail messages is highly overall correlated with voice mail planHigh correlation
total day minutes is highly overall correlated with total day chargeHigh correlation
total day charge is highly overall correlated with total day minutesHigh correlation
total eve minutes is highly overall correlated with total eve chargeHigh correlation
total eve charge is highly overall correlated with total eve minutesHigh correlation
total night minutes is highly overall correlated with total night chargeHigh correlation
total night charge is highly overall correlated with total night minutesHigh correlation
total intl minutes is highly overall correlated with total intl chargeHigh correlation
total intl charge is highly overall correlated with total intl minutesHigh correlation
voice mail plan is highly overall correlated with number vmail messagesHigh correlation
phone number is uniformly distributedUniform
phone number has unique valuesUnique
number vmail messages has 2411 (72.3%) zerosZeros
customer service calls has 697 (20.9%) zerosZeros

Reproduction

Analysis started2022-11-27 06:14:12.664054
Analysis finished2022-11-27 06:15:06.237302
Duration53.57 seconds
Software versionpandas-profiling vv3.5.0
Download configurationconfig.json

Variables

state
Categorical

Distinct51
Distinct (%)1.5%
Missing0
Missing (%)0.0%
Memory size26.2 KiB
WV
 
106
MN
 
84
NY
 
83
AL
 
80
WI
 
78
Other values (46)
2902 

Length

Max length2
Median length2
Mean length2
Min length2

Characters and Unicode

Total characters6666
Distinct characters24
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowKS
2nd rowOH
3rd rowNJ
4th rowOH
5th rowOK

Common Values

ValueCountFrequency (%)
WV 106
 
3.2%
MN 84
 
2.5%
NY 83
 
2.5%
AL 80
 
2.4%
WI 78
 
2.3%
OH 78
 
2.3%
OR 78
 
2.3%
WY 77
 
2.3%
VA 77
 
2.3%
CT 74
 
2.2%
Other values (41) 2518
75.5%

Length

2022-11-27T11:45:06.296447image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
wv 106
 
3.2%
mn 84
 
2.5%
ny 83
 
2.5%
al 80
 
2.4%
wi 78
 
2.3%
oh 78
 
2.3%
or 78
 
2.3%
wy 77
 
2.3%
va 77
 
2.3%
ct 74
 
2.2%
Other values (41) 2518
75.5%

Most occurring characters

ValueCountFrequency (%)
N 734
 
11.0%
A 687
 
10.3%
M 612
 
9.2%
I 515
 
7.7%
T 412
 
6.2%
D 380
 
5.7%
C 356
 
5.3%
O 346
 
5.2%
W 327
 
4.9%
V 322
 
4.8%
Other values (14) 1975
29.6%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 6666
100.0%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
N 734
 
11.0%
A 687
 
10.3%
M 612
 
9.2%
I 515
 
7.7%
T 412
 
6.2%
D 380
 
5.7%
C 356
 
5.3%
O 346
 
5.2%
W 327
 
4.9%
V 322
 
4.8%
Other values (14) 1975
29.6%

Most occurring scripts

ValueCountFrequency (%)
Latin 6666
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
N 734
 
11.0%
A 687
 
10.3%
M 612
 
9.2%
I 515
 
7.7%
T 412
 
6.2%
D 380
 
5.7%
C 356
 
5.3%
O 346
 
5.2%
W 327
 
4.9%
V 322
 
4.8%
Other values (14) 1975
29.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII 6666
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
N 734
 
11.0%
A 687
 
10.3%
M 612
 
9.2%
I 515
 
7.7%
T 412
 
6.2%
D 380
 
5.7%
C 356
 
5.3%
O 346
 
5.2%
W 327
 
4.9%
V 322
 
4.8%
Other values (14) 1975
29.6%

account length
Real number (ℝ)

Distinct212
Distinct (%)6.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean101.06481
Minimum1
Maximum243
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size26.2 KiB
2022-11-27T11:45:06.453923image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile35
Q174
median101
Q3127
95-th percentile167
Maximum243
Range242
Interquartile range (IQR)53

Descriptive statistics

Standard deviation39.822106
Coefficient of variation (CV)0.39402545
Kurtosis-0.10783598
Mean101.06481
Median Absolute Deviation (MAD)27
Skewness0.096606294
Sum336849
Variance1585.8001
MonotonicityNot monotonic
2022-11-27T11:45:06.668044image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
105 43
 
1.3%
87 42
 
1.3%
101 40
 
1.2%
93 40
 
1.2%
90 39
 
1.2%
95 38
 
1.1%
86 38
 
1.1%
100 37
 
1.1%
116 37
 
1.1%
112 36
 
1.1%
Other values (202) 2943
88.3%
ValueCountFrequency (%)
1 8
0.2%
2 1
 
< 0.1%
3 5
0.2%
4 1
 
< 0.1%
5 1
 
< 0.1%
6 2
 
0.1%
7 2
 
0.1%
8 1
 
< 0.1%
9 3
 
0.1%
10 3
 
0.1%
ValueCountFrequency (%)
243 1
 
< 0.1%
232 1
 
< 0.1%
225 2
0.1%
224 2
0.1%
221 1
 
< 0.1%
217 2
0.1%
215 1
 
< 0.1%
212 2
0.1%
210 2
0.1%
209 3
0.1%

area code
Categorical

Distinct3
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size26.2 KiB
415
1655 
510
840 
408
838 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters9999
Distinct characters5
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row415
2nd row415
3rd row415
4th row408
5th row415

Common Values

ValueCountFrequency (%)
415 1655
49.7%
510 840
25.2%
408 838
25.1%

Length

2022-11-27T11:45:06.939490image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2022-11-27T11:45:07.114079image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
ValueCountFrequency (%)
415 1655
49.7%
510 840
25.2%
408 838
25.1%

Most occurring characters

ValueCountFrequency (%)
1 2495
25.0%
5 2495
25.0%
4 2493
24.9%
0 1678
16.8%
8 838
 
8.4%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 9999
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 2495
25.0%
5 2495
25.0%
4 2493
24.9%
0 1678
16.8%
8 838
 
8.4%

Most occurring scripts

ValueCountFrequency (%)
Common 9999
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 2495
25.0%
5 2495
25.0%
4 2493
24.9%
0 1678
16.8%
8 838
 
8.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 9999
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 2495
25.0%
5 2495
25.0%
4 2493
24.9%
0 1678
16.8%
8 838
 
8.4%

phone number
Categorical

HIGH CARDINALITY
UNIFORM
UNIQUE

Distinct3333
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size26.2 KiB
382-4657
 
1
348-7071
 
1
389-6082
 
1
415-3689
 
1
379-2503
 
1
Other values (3328)
3328 

Length

Max length8
Median length8
Mean length8
Min length8

Characters and Unicode

Total characters26664
Distinct characters11
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique3333 ?
Unique (%)100.0%

Sample

1st row382-4657
2nd row371-7191
3rd row358-1921
4th row375-9999
5th row330-6626

Common Values

ValueCountFrequency (%)
382-4657 1
 
< 0.1%
348-7071 1
 
< 0.1%
389-6082 1
 
< 0.1%
415-3689 1
 
< 0.1%
379-2503 1
 
< 0.1%
396-1106 1
 
< 0.1%
379-4372 1
 
< 0.1%
336-3738 1
 
< 0.1%
380-2600 1
 
< 0.1%
345-4473 1
 
< 0.1%
Other values (3323) 3323
99.7%

Length

2022-11-27T11:45:07.253665image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
382-4657 1
 
< 0.1%
407-7507 1
 
< 0.1%
363-1107 1
 
< 0.1%
358-1921 1
 
< 0.1%
375-9999 1
 
< 0.1%
330-6626 1
 
< 0.1%
391-8027 1
 
< 0.1%
355-9993 1
 
< 0.1%
329-9001 1
 
< 0.1%
335-4719 1
 
< 0.1%
Other values (3323) 3323
99.7%

Most occurring characters

ValueCountFrequency (%)
3 4626
17.3%
- 3333
12.5%
4 2820
10.6%
9 2090
7.8%
6 2070
7.8%
5 2050
7.7%
7 2037
7.6%
8 2005
7.5%
1 1979
7.4%
2 1891
7.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 23331
87.5%
Dash Punctuation 3333
 
12.5%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
3 4626
19.8%
4 2820
12.1%
9 2090
9.0%
6 2070
8.9%
5 2050
8.8%
7 2037
8.7%
8 2005
8.6%
1 1979
8.5%
2 1891
8.1%
0 1763
 
7.6%
Dash Punctuation
ValueCountFrequency (%)
- 3333
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 26664
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
3 4626
17.3%
- 3333
12.5%
4 2820
10.6%
9 2090
7.8%
6 2070
7.8%
5 2050
7.7%
7 2037
7.6%
8 2005
7.5%
1 1979
7.4%
2 1891
7.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 26664
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
3 4626
17.3%
- 3333
12.5%
4 2820
10.6%
9 2090
7.8%
6 2070
7.8%
5 2050
7.7%
7 2037
7.6%
8 2005
7.5%
1 1979
7.4%
2 1891
7.1%
Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size3.4 KiB
False
3010 
True
323 
ValueCountFrequency (%)
False 3010
90.3%
True 323
 
9.7%
2022-11-27T11:45:07.401737image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size3.4 KiB
False
2411 
True
922 
ValueCountFrequency (%)
False 2411
72.3%
True 922
 
27.7%
2022-11-27T11:45:07.577472image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

number vmail messages
Real number (ℝ)

HIGH CORRELATION
ZEROS

Distinct46
Distinct (%)1.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean8.0990099
Minimum0
Maximum51
Zeros2411
Zeros (%)72.3%
Negative0
Negative (%)0.0%
Memory size26.2 KiB
2022-11-27T11:45:07.700733image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q320
95-th percentile36
Maximum51
Range51
Interquartile range (IQR)20

Descriptive statistics

Standard deviation13.688365
Coefficient of variation (CV)1.6901282
Kurtosis-0.051128539
Mean8.0990099
Median Absolute Deviation (MAD)0
Skewness1.2648236
Sum26994
Variance187.37135
MonotonicityNot monotonic
2022-11-27T11:45:07.854464image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=46)
ValueCountFrequency (%)
0 2411
72.3%
31 60
 
1.8%
29 53
 
1.6%
28 51
 
1.5%
33 46
 
1.4%
27 44
 
1.3%
30 44
 
1.3%
24 42
 
1.3%
26 41
 
1.2%
32 41
 
1.2%
Other values (36) 500
 
15.0%
ValueCountFrequency (%)
0 2411
72.3%
4 1
 
< 0.1%
8 2
 
0.1%
9 2
 
0.1%
10 1
 
< 0.1%
11 2
 
0.1%
12 6
 
0.2%
13 4
 
0.1%
14 7
 
0.2%
15 9
 
0.3%
ValueCountFrequency (%)
51 1
 
< 0.1%
50 2
 
0.1%
49 1
 
< 0.1%
48 2
 
0.1%
47 3
 
0.1%
46 4
 
0.1%
45 6
 
0.2%
44 7
0.2%
43 9
0.3%
42 15
0.5%

total day minutes
Real number (ℝ)

Distinct1667
Distinct (%)50.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean179.7751
Minimum0
Maximum350.8
Zeros2
Zeros (%)0.1%
Negative0
Negative (%)0.0%
Memory size26.2 KiB
2022-11-27T11:45:08.155775image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile89.92
Q1143.7
median179.4
Q3216.4
95-th percentile270.74
Maximum350.8
Range350.8
Interquartile range (IQR)72.7

Descriptive statistics

Standard deviation54.467389
Coefficient of variation (CV)0.30297516
Kurtosis-0.019940379
Mean179.7751
Median Absolute Deviation (MAD)36.3
Skewness-0.029077067
Sum599190.4
Variance2966.6965
MonotonicityNot monotonic
2022-11-27T11:45:08.324317image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
154 8
 
0.2%
159.5 8
 
0.2%
174.5 8
 
0.2%
183.4 7
 
0.2%
175.4 7
 
0.2%
162.3 7
 
0.2%
178.7 6
 
0.2%
194.8 6
 
0.2%
189.3 6
 
0.2%
146.3 6
 
0.2%
Other values (1657) 3264
97.9%
ValueCountFrequency (%)
0 2
0.1%
2.6 1
< 0.1%
7.8 1
< 0.1%
7.9 1
< 0.1%
12.5 1
< 0.1%
17.6 1
< 0.1%
18.9 1
< 0.1%
19.5 1
< 0.1%
25.9 1
< 0.1%
27 1
< 0.1%
ValueCountFrequency (%)
350.8 1
< 0.1%
346.8 1
< 0.1%
345.3 1
< 0.1%
337.4 1
< 0.1%
335.5 1
< 0.1%
334.3 1
< 0.1%
332.9 1
< 0.1%
329.8 1
< 0.1%
328.1 1
< 0.1%
326.5 1
< 0.1%

total day calls
Real number (ℝ)

Distinct119
Distinct (%)3.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean100.43564
Minimum0
Maximum165
Zeros2
Zeros (%)0.1%
Negative0
Negative (%)0.0%
Memory size26.2 KiB
2022-11-27T11:45:08.496470image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile67
Q187
median101
Q3114
95-th percentile133
Maximum165
Range165
Interquartile range (IQR)27

Descriptive statistics

Standard deviation20.069084
Coefficient of variation (CV)0.19982034
Kurtosis0.24318152
Mean100.43564
Median Absolute Deviation (MAD)13
Skewness-0.11178664
Sum334752
Variance402.76814
MonotonicityNot monotonic
2022-11-27T11:45:08.660033image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
102 78
 
2.3%
105 75
 
2.3%
95 69
 
2.1%
107 69
 
2.1%
104 68
 
2.0%
108 67
 
2.0%
97 67
 
2.0%
106 66
 
2.0%
112 66
 
2.0%
110 66
 
2.0%
Other values (109) 2642
79.3%
ValueCountFrequency (%)
0 2
0.1%
30 1
 
< 0.1%
35 1
 
< 0.1%
36 1
 
< 0.1%
40 2
0.1%
42 2
0.1%
44 3
0.1%
45 3
0.1%
47 2
0.1%
48 3
0.1%
ValueCountFrequency (%)
165 1
 
< 0.1%
163 1
 
< 0.1%
160 1
 
< 0.1%
158 3
0.1%
157 1
 
< 0.1%
156 1
 
< 0.1%
152 1
 
< 0.1%
151 5
0.2%
150 6
0.2%
149 1
 
< 0.1%

total day charge
Real number (ℝ)

Distinct1667
Distinct (%)50.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean30.562307
Minimum0
Maximum59.64
Zeros2
Zeros (%)0.1%
Negative0
Negative (%)0.0%
Memory size26.2 KiB
2022-11-27T11:45:08.906147image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile15.288
Q124.43
median30.5
Q336.79
95-th percentile46.028
Maximum59.64
Range59.64
Interquartile range (IQR)12.36

Descriptive statistics

Standard deviation9.2594346
Coefficient of variation (CV)0.30296909
Kurtosis-0.019811787
Mean30.562307
Median Absolute Deviation (MAD)6.17
Skewness-0.029083268
Sum101864.17
Variance85.737128
MonotonicityNot monotonic
2022-11-27T11:45:09.071845image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
26.18 8
 
0.2%
27.12 8
 
0.2%
29.67 8
 
0.2%
31.18 7
 
0.2%
29.82 7
 
0.2%
27.59 7
 
0.2%
30.38 6
 
0.2%
33.12 6
 
0.2%
32.18 6
 
0.2%
24.87 6
 
0.2%
Other values (1657) 3264
97.9%
ValueCountFrequency (%)
0 2
0.1%
0.44 1
< 0.1%
1.33 1
< 0.1%
1.34 1
< 0.1%
2.13 1
< 0.1%
2.99 1
< 0.1%
3.21 1
< 0.1%
3.32 1
< 0.1%
4.4 1
< 0.1%
4.59 1
< 0.1%
ValueCountFrequency (%)
59.64 1
< 0.1%
58.96 1
< 0.1%
58.7 1
< 0.1%
57.36 1
< 0.1%
57.04 1
< 0.1%
56.83 1
< 0.1%
56.59 1
< 0.1%
56.07 1
< 0.1%
55.78 1
< 0.1%
55.51 1
< 0.1%

total eve minutes
Real number (ℝ)

Distinct1611
Distinct (%)48.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean200.98035
Minimum0
Maximum363.7
Zeros1
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size26.2 KiB
2022-11-27T11:45:09.345288image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile118.8
Q1166.6
median201.4
Q3235.3
95-th percentile284.3
Maximum363.7
Range363.7
Interquartile range (IQR)68.7

Descriptive statistics

Standard deviation50.713844
Coefficient of variation (CV)0.25233235
Kurtosis0.025629753
Mean200.98035
Median Absolute Deviation (MAD)34.4
Skewness-0.023877456
Sum669867.5
Variance2571.894
MonotonicityNot monotonic
2022-11-27T11:45:09.507247image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
169.9 9
 
0.3%
167.2 7
 
0.2%
180.5 7
 
0.2%
201 7
 
0.2%
161.7 7
 
0.2%
209.4 7
 
0.2%
230.9 7
 
0.2%
220.6 7
 
0.2%
195.5 7
 
0.2%
230 6
 
0.2%
Other values (1601) 3262
97.9%
ValueCountFrequency (%)
0 1
< 0.1%
31.2 1
< 0.1%
42.2 1
< 0.1%
42.5 1
< 0.1%
43.9 1
< 0.1%
48.1 1
< 0.1%
49.2 1
< 0.1%
52.9 1
< 0.1%
56 1
< 0.1%
58.6 1
< 0.1%
ValueCountFrequency (%)
363.7 1
< 0.1%
361.8 1
< 0.1%
354.2 1
< 0.1%
351.6 1
< 0.1%
350.9 1
< 0.1%
350.5 1
< 0.1%
348.5 1
< 0.1%
347.3 1
< 0.1%
341.3 1
< 0.1%
339.9 1
< 0.1%

total eve calls
Real number (ℝ)

Distinct123
Distinct (%)3.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean100.11431
Minimum0
Maximum170
Zeros1
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size26.2 KiB
2022-11-27T11:45:09.668753image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile67
Q187
median100
Q3114
95-th percentile133
Maximum170
Range170
Interquartile range (IQR)27

Descriptive statistics

Standard deviation19.922625
Coefficient of variation (CV)0.19899877
Kurtosis0.20615647
Mean100.11431
Median Absolute Deviation (MAD)13
Skewness-0.055563139
Sum333681
Variance396.911
MonotonicityNot monotonic
2022-11-27T11:45:09.805796image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
105 80
 
2.4%
94 79
 
2.4%
108 71
 
2.1%
102 70
 
2.1%
97 70
 
2.1%
88 69
 
2.1%
101 68
 
2.0%
109 67
 
2.0%
98 66
 
2.0%
111 65
 
2.0%
Other values (113) 2628
78.8%
ValueCountFrequency (%)
0 1
 
< 0.1%
12 1
 
< 0.1%
36 1
 
< 0.1%
37 1
 
< 0.1%
42 1
 
< 0.1%
43 1
 
< 0.1%
44 1
 
< 0.1%
45 1
 
< 0.1%
46 3
0.1%
48 6
0.2%
ValueCountFrequency (%)
170 1
 
< 0.1%
168 1
 
< 0.1%
164 1
 
< 0.1%
159 1
 
< 0.1%
157 1
 
< 0.1%
156 1
 
< 0.1%
155 3
0.1%
154 2
 
0.1%
153 1
 
< 0.1%
152 6
0.2%

total eve charge
Real number (ℝ)

Distinct1440
Distinct (%)43.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean17.08354
Minimum0
Maximum30.91
Zeros1
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size26.2 KiB
2022-11-27T11:45:09.954985image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile10.1
Q114.16
median17.12
Q320
95-th percentile24.17
Maximum30.91
Range30.91
Interquartile range (IQR)5.84

Descriptive statistics

Standard deviation4.3106676
Coefficient of variation (CV)0.25232871
Kurtosis0.025487405
Mean17.08354
Median Absolute Deviation (MAD)2.92
Skewness-0.023857989
Sum56939.44
Variance18.581856
MonotonicityNot monotonic
2022-11-27T11:45:10.222432image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
14.25 11
 
0.3%
16.12 11
 
0.3%
15.9 10
 
0.3%
17.09 9
 
0.3%
18.62 9
 
0.3%
17.99 9
 
0.3%
14.44 9
 
0.3%
18.96 8
 
0.2%
16.35 8
 
0.2%
16.97 8
 
0.2%
Other values (1430) 3241
97.2%
ValueCountFrequency (%)
0 1
< 0.1%
2.65 1
< 0.1%
3.59 1
< 0.1%
3.61 1
< 0.1%
3.73 1
< 0.1%
4.09 1
< 0.1%
4.18 1
< 0.1%
4.5 1
< 0.1%
4.76 1
< 0.1%
4.98 1
< 0.1%
ValueCountFrequency (%)
30.91 1
< 0.1%
30.75 1
< 0.1%
30.11 1
< 0.1%
29.89 1
< 0.1%
29.83 1
< 0.1%
29.79 1
< 0.1%
29.62 1
< 0.1%
29.52 1
< 0.1%
29.01 1
< 0.1%
28.89 1
< 0.1%

total night minutes
Real number (ℝ)

Distinct1591
Distinct (%)47.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean200.87204
Minimum23.2
Maximum395
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size26.2 KiB
2022-11-27T11:45:10.416546image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum23.2
5-th percentile118.18
Q1167
median201.2
Q3235.3
95-th percentile282.84
Maximum395
Range371.8
Interquartile range (IQR)68.3

Descriptive statistics

Standard deviation50.573847
Coefficient of variation (CV)0.25177146
Kurtosis0.085816078
Mean200.87204
Median Absolute Deviation (MAD)34.2
Skewness0.0089212911
Sum669506.5
Variance2557.714
MonotonicityNot monotonic
2022-11-27T11:45:10.776901image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
191.4 8
 
0.2%
210 8
 
0.2%
188.2 8
 
0.2%
197.4 8
 
0.2%
214.6 8
 
0.2%
193.6 7
 
0.2%
206.1 7
 
0.2%
194.3 7
 
0.2%
214.7 7
 
0.2%
231.5 7
 
0.2%
Other values (1581) 3258
97.7%
ValueCountFrequency (%)
23.2 1
< 0.1%
43.7 1
< 0.1%
45 1
< 0.1%
47.4 1
< 0.1%
50.1 2
0.1%
53.3 1
< 0.1%
54 1
< 0.1%
54.5 1
< 0.1%
56.6 1
< 0.1%
57.5 1
< 0.1%
ValueCountFrequency (%)
395 1
< 0.1%
381.9 1
< 0.1%
377.5 1
< 0.1%
367.7 1
< 0.1%
364.9 1
< 0.1%
364.3 1
< 0.1%
354.9 1
< 0.1%
352.5 1
< 0.1%
352.2 1
< 0.1%
350.2 1
< 0.1%

total night calls
Real number (ℝ)

Distinct120
Distinct (%)3.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean100.10771
Minimum33
Maximum175
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size26.2 KiB
2022-11-27T11:45:11.114625image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum33
5-th percentile68
Q187
median100
Q3113
95-th percentile132
Maximum175
Range142
Interquartile range (IQR)26

Descriptive statistics

Standard deviation19.568609
Coefficient of variation (CV)0.19547555
Kurtosis-0.072019579
Mean100.10771
Median Absolute Deviation (MAD)13
Skewness0.03249957
Sum333659
Variance382.93047
MonotonicityNot monotonic
2022-11-27T11:45:11.435188image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
105 84
 
2.5%
104 78
 
2.3%
91 76
 
2.3%
102 72
 
2.2%
100 69
 
2.1%
106 69
 
2.1%
98 67
 
2.0%
94 66
 
2.0%
103 65
 
2.0%
95 64
 
1.9%
Other values (110) 2623
78.7%
ValueCountFrequency (%)
33 1
< 0.1%
36 1
< 0.1%
38 1
< 0.1%
42 2
0.1%
44 1
< 0.1%
46 1
< 0.1%
48 1
< 0.1%
49 2
0.1%
50 2
0.1%
51 2
0.1%
ValueCountFrequency (%)
175 1
 
< 0.1%
166 1
 
< 0.1%
164 1
 
< 0.1%
158 1
 
< 0.1%
157 2
0.1%
156 2
0.1%
155 2
0.1%
154 2
0.1%
153 3
0.1%
152 3
0.1%

total night charge
Real number (ℝ)

Distinct933
Distinct (%)28.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean9.0393249
Minimum1.04
Maximum17.77
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size26.2 KiB
2022-11-27T11:45:11.693110image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum1.04
5-th percentile5.316
Q17.52
median9.05
Q310.59
95-th percentile12.73
Maximum17.77
Range16.73
Interquartile range (IQR)3.07

Descriptive statistics

Standard deviation2.2758728
Coefficient of variation (CV)0.25177465
Kurtosis0.08566318
Mean9.0393249
Median Absolute Deviation (MAD)1.54
Skewness0.0088862368
Sum30128.07
Variance5.1795972
MonotonicityNot monotonic
2022-11-27T11:45:11.889064image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
9.66 15
 
0.5%
9.45 15
 
0.5%
8.47 14
 
0.4%
8.88 14
 
0.4%
7.69 13
 
0.4%
8.64 12
 
0.4%
10.8 11
 
0.3%
10.49 11
 
0.3%
10.35 11
 
0.3%
8.57 11
 
0.3%
Other values (923) 3206
96.2%
ValueCountFrequency (%)
1.04 1
< 0.1%
1.97 1
< 0.1%
2.03 1
< 0.1%
2.13 1
< 0.1%
2.25 2
0.1%
2.4 1
< 0.1%
2.43 1
< 0.1%
2.45 1
< 0.1%
2.55 1
< 0.1%
2.59 1
< 0.1%
ValueCountFrequency (%)
17.77 1
< 0.1%
17.19 1
< 0.1%
16.99 1
< 0.1%
16.55 1
< 0.1%
16.42 1
< 0.1%
16.39 1
< 0.1%
15.97 1
< 0.1%
15.86 1
< 0.1%
15.85 1
< 0.1%
15.76 1
< 0.1%

total intl minutes
Real number (ℝ)

Distinct162
Distinct (%)4.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean10.237294
Minimum0
Maximum20
Zeros18
Zeros (%)0.5%
Negative0
Negative (%)0.0%
Memory size26.2 KiB
2022-11-27T11:45:12.177571image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile5.7
Q18.5
median10.3
Q312.1
95-th percentile14.7
Maximum20
Range20
Interquartile range (IQR)3.6

Descriptive statistics

Standard deviation2.7918395
Coefficient of variation (CV)0.27271265
Kurtosis0.60918476
Mean10.237294
Median Absolute Deviation (MAD)1.8
Skewness-0.24513594
Sum34120.9
Variance7.7943681
MonotonicityNot monotonic
2022-11-27T11:45:12.447727image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
10 62
 
1.9%
11.3 59
 
1.8%
9.8 56
 
1.7%
10.9 56
 
1.7%
10.1 53
 
1.6%
10.6 53
 
1.6%
10.2 53
 
1.6%
11 52
 
1.6%
11.1 52
 
1.6%
9.7 51
 
1.5%
Other values (152) 2786
83.6%
ValueCountFrequency (%)
0 18
0.5%
1.1 1
 
< 0.1%
1.3 1
 
< 0.1%
2 2
 
0.1%
2.1 2
 
0.1%
2.2 1
 
< 0.1%
2.4 1
 
< 0.1%
2.5 1
 
< 0.1%
2.6 1
 
< 0.1%
2.7 1
 
< 0.1%
ValueCountFrequency (%)
20 1
 
< 0.1%
18.9 1
 
< 0.1%
18.4 1
 
< 0.1%
18.3 1
 
< 0.1%
18.2 2
0.1%
18 3
0.1%
17.9 1
 
< 0.1%
17.8 2
0.1%
17.6 2
0.1%
17.5 3
0.1%

total intl calls
Real number (ℝ)

Distinct21
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.4794479
Minimum0
Maximum20
Zeros18
Zeros (%)0.5%
Negative0
Negative (%)0.0%
Memory size26.2 KiB
2022-11-27T11:45:12.582577image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q13
median4
Q36
95-th percentile9
Maximum20
Range20
Interquartile range (IQR)3

Descriptive statistics

Standard deviation2.4612143
Coefficient of variation (CV)0.54944589
Kurtosis3.083589
Mean4.4794479
Median Absolute Deviation (MAD)1
Skewness1.3214782
Sum14930
Variance6.0575757
MonotonicityNot monotonic
2022-11-27T11:45:12.691550image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=21)
ValueCountFrequency (%)
3 668
20.0%
4 619
18.6%
2 489
14.7%
5 472
14.2%
6 336
10.1%
7 218
 
6.5%
1 160
 
4.8%
8 116
 
3.5%
9 109
 
3.3%
10 50
 
1.5%
Other values (11) 96
 
2.9%
ValueCountFrequency (%)
0 18
 
0.5%
1 160
 
4.8%
2 489
14.7%
3 668
20.0%
4 619
18.6%
5 472
14.2%
6 336
10.1%
7 218
 
6.5%
8 116
 
3.5%
9 109
 
3.3%
ValueCountFrequency (%)
20 1
 
< 0.1%
19 1
 
< 0.1%
18 3
 
0.1%
17 1
 
< 0.1%
16 2
 
0.1%
15 7
 
0.2%
14 6
 
0.2%
13 14
0.4%
12 15
0.5%
11 28
0.8%

total intl charge
Real number (ℝ)

Distinct162
Distinct (%)4.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.7645815
Minimum0
Maximum5.4
Zeros18
Zeros (%)0.5%
Negative0
Negative (%)0.0%
Memory size26.2 KiB
2022-11-27T11:45:12.820554image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1.54
Q12.3
median2.78
Q33.27
95-th percentile3.97
Maximum5.4
Range5.4
Interquartile range (IQR)0.97

Descriptive statistics

Standard deviation0.75377261
Coefficient of variation (CV)0.27265343
Kurtosis0.60961043
Mean2.7645815
Median Absolute Deviation (MAD)0.48
Skewness-0.24528651
Sum9214.35
Variance0.56817315
MonotonicityNot monotonic
2022-11-27T11:45:12.964827image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2.7 62
 
1.9%
3.05 59
 
1.8%
2.65 56
 
1.7%
2.94 56
 
1.7%
2.73 53
 
1.6%
2.86 53
 
1.6%
2.75 53
 
1.6%
2.97 52
 
1.6%
3 52
 
1.6%
2.62 51
 
1.5%
Other values (152) 2786
83.6%
ValueCountFrequency (%)
0 18
0.5%
0.3 1
 
< 0.1%
0.35 1
 
< 0.1%
0.54 2
 
0.1%
0.57 2
 
0.1%
0.59 1
 
< 0.1%
0.65 1
 
< 0.1%
0.68 1
 
< 0.1%
0.7 1
 
< 0.1%
0.73 1
 
< 0.1%
ValueCountFrequency (%)
5.4 1
 
< 0.1%
5.1 1
 
< 0.1%
4.97 1
 
< 0.1%
4.94 1
 
< 0.1%
4.91 2
0.1%
4.86 3
0.1%
4.83 1
 
< 0.1%
4.81 2
0.1%
4.75 2
0.1%
4.73 3
0.1%

customer service calls
Real number (ℝ)

Distinct10
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.5628563
Minimum0
Maximum9
Zeros697
Zeros (%)20.9%
Negative0
Negative (%)0.0%
Memory size26.2 KiB
2022-11-27T11:45:13.085827image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11
median1
Q32
95-th percentile4
Maximum9
Range9
Interquartile range (IQR)1

Descriptive statistics

Standard deviation1.315491
Coefficient of variation (CV)0.84172234
Kurtosis1.7309137
Mean1.5628563
Median Absolute Deviation (MAD)1
Skewness1.0913595
Sum5209
Variance1.7305167
MonotonicityNot monotonic
2022-11-27T11:45:13.188584image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%)
1 1181
35.4%
2 759
22.8%
0 697
20.9%
3 429
 
12.9%
4 166
 
5.0%
5 66
 
2.0%
6 22
 
0.7%
7 9
 
0.3%
9 2
 
0.1%
8 2
 
0.1%
ValueCountFrequency (%)
0 697
20.9%
1 1181
35.4%
2 759
22.8%
3 429
 
12.9%
4 166
 
5.0%
5 66
 
2.0%
6 22
 
0.7%
7 9
 
0.3%
8 2
 
0.1%
9 2
 
0.1%
ValueCountFrequency (%)
9 2
 
0.1%
8 2
 
0.1%
7 9
 
0.3%
6 22
 
0.7%
5 66
 
2.0%
4 166
 
5.0%
3 429
 
12.9%
2 759
22.8%
1 1181
35.4%
0 697
20.9%

churn
Boolean

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size3.4 KiB
False
2850 
True
483 
ValueCountFrequency (%)
False 2850
85.5%
True 483
 
14.5%
2022-11-27T11:45:13.299702image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Interactions

2022-11-27T11:45:02.661354image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-27T11:44:18.061063image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-27T11:44:20.769512image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-27T11:44:23.867831image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-27T11:44:26.782879image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-27T11:44:30.082560image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-27T11:44:33.109638image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-27T11:44:35.985043image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-27T11:44:39.651104image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-27T11:44:42.473478image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-27T11:44:45.653709image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-27T11:44:49.380618image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-27T11:44:52.945318image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-27T11:44:56.266835image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-27T11:44:59.982884image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-27T11:45:02.921501image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-27T11:44:18.301982image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-27T11:44:20.902338image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-27T11:44:24.012505image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-27T11:44:27.006548image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-27T11:44:30.278394image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-27T11:44:33.255622image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-27T11:44:36.247777image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-27T11:44:39.861649image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-27T11:44:42.705536image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-27T11:44:45.863032image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-27T11:44:49.561364image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-27T11:44:53.111794image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-27T11:44:56.618009image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-27T11:45:00.166671image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-27T11:45:03.178671image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-27T11:44:18.519959image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-27T11:44:21.105105image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-27T11:44:24.154220image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-27T11:44:27.167425image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-27T11:44:30.425279image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-27T11:44:33.396491image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-27T11:44:36.444613image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-27T11:44:40.023432image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-27T11:44:42.885860image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-27T11:44:46.046302image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-27T11:44:49.848047image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-27T11:44:53.259798image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-27T11:44:56.879818image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-27T11:45:00.334413image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-27T11:45:03.354476image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-27T11:44:18.673681image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-27T11:44:21.266185image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-27T11:44:24.296234image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-27T11:44:27.300295image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-27T11:44:30.641904image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-27T11:44:33.550150image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-27T11:44:36.870911image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-27T11:44:40.220394image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-27T11:44:43.056141image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-27T11:44:46.302138image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-27T11:44:50.106707image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-27T11:44:53.539277image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-27T11:44:57.083453image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-27T11:45:00.620983image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-27T11:45:03.487152image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-27T11:44:18.816348image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-27T11:44:21.478053image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-27T11:44:24.433890image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-27T11:44:27.432010image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-27T11:44:30.830559image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-27T11:44:33.739049image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-27T11:44:37.129690image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-27T11:44:40.398145image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-27T11:44:43.297081image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-27T11:44:46.541301image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-27T11:44:50.294553image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-27T11:44:53.721168image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-27T11:44:57.321018image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-27T11:45:00.752830image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-27T11:45:03.619936image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-27T11:44:19.024045image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-27T11:44:21.693594image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-27T11:44:24.573929image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-27T11:44:27.775577image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-27T11:44:30.983390image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-27T11:44:33.991570image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-27T11:44:37.416321image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-27T11:44:40.531202image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-27T11:44:43.524430image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-27T11:44:46.838258image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-27T11:44:50.479840image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-27T11:44:53.868919image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-27T11:44:57.600649image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-27T11:45:00.882293image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-27T11:45:03.746851image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-27T11:44:19.350888image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-27T11:44:21.903458image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-27T11:44:24.838604image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-27T11:44:27.915375image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-27T11:44:31.166463image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-27T11:44:34.270223image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-27T11:44:37.702983image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-27T11:44:40.665493image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-27T11:44:43.816850image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-27T11:44:47.172291image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-27T11:44:50.744968image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-27T11:44:54.094301image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-27T11:44:57.817286image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-27T11:45:01.011499image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-27T11:45:03.927649image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-27T11:44:19.582337image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-27T11:44:22.154166image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-27T11:44:25.112071image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-27T11:44:28.069253image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-27T11:44:31.396864image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-27T11:44:34.445939image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-27T11:44:37.919927image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-27T11:44:40.868761image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-27T11:44:43.991792image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-27T11:44:47.476600image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-27T11:44:51.022667image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-27T11:44:54.323203image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-27T11:44:58.056004image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-27T11:45:01.166253image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-27T11:45:04.179327image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-27T11:44:19.724576image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-27T11:44:22.308741image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-27T11:44:25.294105image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-27T11:44:28.307984image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-27T11:44:31.661584image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-27T11:44:34.584756image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-27T11:44:38.206572image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-27T11:44:41.059987image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-27T11:44:44.222633image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-27T11:44:47.746033image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-27T11:44:51.275261image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-27T11:44:54.538238image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-27T11:44:58.285788image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-27T11:45:01.334143image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-27T11:45:04.355220image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-27T11:44:19.967635image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-27T11:44:22.466592image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-27T11:44:25.433605image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-27T11:44:28.607580image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-27T11:44:31.858389image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-27T11:44:34.821656image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-27T11:44:38.553967image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-27T11:44:41.444235image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-27T11:44:44.411052image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-27T11:44:47.941788image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-27T11:44:51.504926image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-27T11:44:54.713667image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-27T11:44:58.551396image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-27T11:45:01.550802image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-27T11:45:04.718650image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-27T11:44:20.112348image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-27T11:44:22.700284image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-27T11:44:25.671370image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-27T11:44:28.895162image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-27T11:44:32.046964image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-27T11:44:35.005610image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-27T11:44:38.813235image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-27T11:44:41.702333image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-27T11:44:44.550382image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-27T11:44:48.226031image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-27T11:44:51.749604image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-27T11:44:54.895593image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-27T11:44:58.719213image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-27T11:45:01.738484image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-27T11:45:04.900456image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-27T11:44:20.246233image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-27T11:44:22.992729image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-27T11:44:25.921912image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-27T11:44:29.179829image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-27T11:44:32.216768image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-27T11:44:35.200120image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-27T11:44:38.950641image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-27T11:44:41.870121image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-27T11:44:44.773643image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-27T11:44:48.490945image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-27T11:44:51.959291image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-27T11:44:55.147170image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-27T11:44:58.886918image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-27T11:45:01.880325image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-27T11:45:05.109113image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-27T11:44:20.372073image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-27T11:44:23.196420image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-27T11:44:26.147719image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-27T11:44:29.466516image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-27T11:44:32.480551image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-27T11:44:35.418799image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-27T11:44:39.170361image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-27T11:44:42.078964image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-27T11:44:44.980110image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-27T11:44:48.714479image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-27T11:44:52.162097image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-27T11:44:55.461736image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-27T11:44:59.118685image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-27T11:45:02.109907image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-27T11:45:05.276779image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-27T11:44:20.504911image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-27T11:44:23.482114image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-27T11:44:26.364391image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-27T11:44:29.755053image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-27T11:44:32.774184image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-27T11:44:35.598762image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-27T11:44:39.320816image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-27T11:44:42.209757image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-27T11:44:45.207336image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-27T11:44:48.919297image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-27T11:44:52.455712image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-27T11:44:55.769373image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-27T11:44:59.418434image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-27T11:45:02.296803image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-27T11:45:05.451617image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-27T11:44:20.647169image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-27T11:44:23.652024image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-27T11:44:26.587178image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-27T11:44:29.935736image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-27T11:44:32.962912image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-27T11:44:35.732482image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-27T11:44:39.481912image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-27T11:44:42.355661image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-27T11:44:45.376516image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-27T11:44:49.197945image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-27T11:44:52.707361image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-27T11:44:55.979077image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-27T11:44:59.726046image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-27T11:45:02.446553image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Correlations

2022-11-27T11:45:13.614967image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Auto

The auto setting is an interpretable pairwise column metric of the following mapping:
  • Variable_type-Variable_type : Method, Range
  • Categorical-Categorical : Cramer's V, [0,1]
  • Numerical-Categorical : Cramer's V, [0,1] (using a discretized numerical column)
  • Numerical-Numerical : Spearman's ρ, [-1,1]
The number of bins used in the discretization for the Numerical-Categorical column pair can be changed using config.correlations["auto"].n_bins. The number of bins affects the granularity of the association you wish to measure.

This configuration uses the recommended metric for each pair of columns.
2022-11-27T11:45:13.978446image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Spearman's ρ

The Spearman's rank correlation coefficient (ρ) is a measure of monotonic correlation between two variables, and is therefore better in catching nonlinear monotonic correlations than Pearson's r. It's value lies between -1 and +1, -1 indicating total negative monotonic correlation, 0 indicating no monotonic correlation and 1 indicating total positive monotonic correlation.

To calculate ρ for two variables X and Y, one divides the covariance of the rank variables of X and Y by the product of their standard deviations.
2022-11-27T11:45:14.271928image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Pearson's r

The Pearson's correlation coefficient (r) is a measure of linear correlation between two variables. It's value lies between -1 and +1, -1 indicating total negative linear correlation, 0 indicating no linear correlation and 1 indicating total positive linear correlation. Furthermore, r is invariant under separate changes in location and scale of the two variables, implying that for a linear function the angle to the x-axis does not affect r.

To calculate r for two variables X and Y, one divides the covariance of X and Y by the product of their standard deviations.
2022-11-27T11:45:14.572324image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Kendall's τ

Similarly to Spearman's rank correlation coefficient, the Kendall rank correlation coefficient (τ) measures ordinal association between two variables. It's value lies between -1 and +1, -1 indicating total negative correlation, 0 indicating no correlation and 1 indicating total positive correlation.

To calculate τ for two variables X and Y, one determines the number of concordant and discordant pairs of observations. τ is given by the number of concordant pairs minus the discordant pairs divided by the total number of pairs.
2022-11-27T11:45:14.816975image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Cramér's V (φc)

Cramér's V is an association measure for nominal random variables. The coefficient ranges from 0 to 1, with 0 indicating independence and 1 indicating perfect association. The empirical estimators used for Cramér's V have been proved to be biased, even for large samples. We use a bias-corrected measure that has been proposed by Bergsma in 2013 that can be found here.
2022-11-27T11:45:14.970730image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Phik (φk)

Phik (φk) is a new and practical correlation coefficient that works consistently between categorical, ordinal and interval variables, captures non-linear dependency and reverts to the Pearson correlation coefficient in case of a bivariate normal input distribution. There is extensive documentation available here.

Missing values

2022-11-27T11:45:05.702058image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
A simple visualization of nullity by column.
2022-11-27T11:45:06.052780image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

stateaccount lengtharea codephone numberinternational planvoice mail plannumber vmail messagestotal day minutestotal day callstotal day chargetotal eve minutestotal eve callstotal eve chargetotal night minutestotal night callstotal night chargetotal intl minutestotal intl callstotal intl chargecustomer service callschurn
0KS128415382-4657noyes25265.111045.07197.49916.78244.79111.0110.032.701False
1OH107415371-7191noyes26161.612327.47195.510316.62254.410311.4513.733.701False
2NJ137415358-1921nono0243.411441.38121.211010.30162.61047.3212.253.290False
3OH84408375-9999yesno0299.47150.9061.9885.26196.9898.866.671.782False
4OK75415330-6626yesno0166.711328.34148.312212.61186.91218.4110.132.733False
5AL118510391-8027yesno0223.49837.98220.610118.75203.91189.186.361.700False
6MA121510355-9993noyes24218.28837.09348.510829.62212.61189.577.572.033False
7MO147415329-9001yesno0157.07926.69103.1948.76211.8969.537.161.920False
8LA117408335-4719nono0184.59731.37351.68029.89215.8909.718.742.351False
9WV141415330-8173yesyes37258.68443.96222.011118.87326.49714.6911.253.020False
stateaccount lengtharea codephone numberinternational planvoice mail plannumber vmail messagestotal day minutestotal day callstotal day chargetotal eve minutestotal eve callstotal eve chargetotal night minutestotal night callstotal night chargetotal intl minutestotal intl callstotal intl chargecustomer service callschurn
3323IN117415362-5899nono0118.412620.13249.39721.19227.05610.2213.633.675True
3324WV159415377-1164nono0169.811428.87197.710516.80193.7828.7211.643.131False
3325OH78408368-8555nono0193.49932.88116.9889.94243.310910.959.342.512False
3326OH96415347-6812nono0106.612818.12284.88724.21178.9928.0514.974.021False
3327SC79415348-3830nono0134.79822.90189.76816.12221.41289.9611.853.192False
3328AZ192415414-4276noyes36156.27726.55215.512618.32279.18312.569.962.672False
3329WV68415370-3271nono0231.15739.29153.45513.04191.31238.619.642.593False
3330RI28510328-8230nono0180.810930.74288.85824.55191.9918.6414.163.812False
3331CT184510364-6381yesno0213.810536.35159.68413.57139.21376.265.0101.352False
3332TN74415400-4344noyes25234.411339.85265.98222.60241.47710.8613.743.700False